Journal of clinical neuroscience : official journal of the Neurosurgical Society of Australasia · 2025
Development and validation of a nomogram for predicting malnutrition risk among patients with Parkinson's disease: A retrospective cohort study.
СтатьяВалидационное исследованиеНазвание и аннотация: Перевод в очереди
Обработка статьи · этапы, ошибки и управление
Загрузка состояния…
Ошибки и ограничения
История · было → стало
Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.
Tang Qiaomin, Ma Weiya, Sun Yuanyuan, Hu Chen, Ma Sumin
Аннотация
OBJECTIVES: This study aimed to establish a nomogram to predict malnutrition risk in patients with Parkinson's disease (PD).
DESIGN: A retrospective cohort study.
SETTING: A Grade III, Class A hospital in Zhejiang Province.
PARTICIPANTS: Patients with primary PD meeting the inclusion criteria were retrospectively identified from the electronic medical record system (January 2023-December 2024) for study inclusion.
METHODS: This study included 21 research variables, encompassing demographic characteristics, physiological features, physical functional status, disease type, and severity. Optimal variables were selected using least absolute shrinkage and selection operator (LASSO) regression and multivariable logistic regression analyses. Internal validation was performed via bootstrap resampling (1,000 iterations), and a nomogram was constructed to predict the risk of malnutrition in patients with PD.
RESULTS: This study included 215 patients with PD for model construction, with a malnutrition prevalence of 35.6 %. The LASSO regression and logistic regression models identified seven significant predictors of malnutrition: lower body mass index, advanced H-Y stage, decreased poorer Unified Parkinson's Disease Rating Scale Part III Maximum Improvement Rate, decreased red blood cell count, reduced total cholesterol, elevated blood urea nitrogen, and dysphagia (P < 0.05). The model achieved an area under the curve of 0.814 (95 % CI: 0.754-0.874), with 70.1 % sensitivity, 76.1 % specificity, and a Youden's index of 0.462, indicating robust predictive performance.
CONCLUSION: The prediction model constructed based on Mini Nutritional Assessment scores demonstrated strong predictive performance and holds significant clinical importance for identifying malnutrition in patients with PD.
DESIGN: A retrospective cohort study.
SETTING: A Grade III, Class A hospital in Zhejiang Province.
PARTICIPANTS: Patients with primary PD meeting the inclusion criteria were retrospectively identified from the electronic medical record system (January 2023-December 2024) for study inclusion.
METHODS: This study included 21 research variables, encompassing demographic characteristics, physiological features, physical functional status, disease type, and severity. Optimal variables were selected using least absolute shrinkage and selection operator (LASSO) regression and multivariable logistic regression analyses. Internal validation was performed via bootstrap resampling (1,000 iterations), and a nomogram was constructed to predict the risk of malnutrition in patients with PD.
RESULTS: This study included 215 patients with PD for model construction, with a malnutrition prevalence of 35.6 %. The LASSO regression and logistic regression models identified seven significant predictors of malnutrition: lower body mass index, advanced H-Y stage, decreased poorer Unified Parkinson's Disease Rating Scale Part III Maximum Improvement Rate, decreased red blood cell count, reduced total cholesterol, elevated blood urea nitrogen, and dysphagia (P < 0.05). The model achieved an area under the curve of 0.814 (95 % CI: 0.754-0.874), with 70.1 % sensitivity, 76.1 % specificity, and a Youden's index of 0.462, indicating robust predictive performance.
CONCLUSION: The prediction model constructed based on Mini Nutritional Assessment scores demonstrated strong predictive performance and holds significant clinical importance for identifying malnutrition in patients with PD.